Lille Apathy Rating Scale-Patient Version in Stroke Survivors: Psychometric Properties and Diagnostic Accuracy.

OBJECTIVES: This study evaluated the factorial structure, psychometric properties, and diagnostic accuracy of the Persian version of the Lille Apathy Rating Scale-Patient version (LARS-P) in stroke survivors. PARTICIPANTS: This study comprised 105 stroke survivors and 41 healthy controls. METHODS AND SETTING: Exploratory factor analysis was used to determine the factors of the LARS-P. The acceptability, reliability, and validity of the LARS-P were also assessed. Agreement between the LARS-P and the Lille Apathy Rating Scale-informed version (LARS-I) was evaluated using the Bland-Altman plot. The diagnostic accuracy of the LARS-P was determined by categorizing stroke survivors into apathetic and nonapathetic groups based on the "diagnostic criteria of apathy." RESULTS: The exploratory factor analysis showed 3 factors (action initiation and social life; novelty and motivation; and emotional and self-awareness), explaining 67.35% of the variance. Cronbach's alpha was 0.85 for between-items and 0.74 for between-subscales. Intra-class correlation coefficient (ICC)2,1 was >0.88 for test-retest and inter-rater reliability. The LARS-P showed moderate to strong correlations with the LARS-I and Neuropsychiatric Inventory-Apathy subscale (r = 0.70-0.82). In addition, the LARS-P had significant moderate correlations with 2 subscales of the Hospital Anxiety and Depression Scale, modified Rankin Scale, Barthel Index, and Lawton Instrumental Activities of Daily Living (r or Æ¿ = 0.47-0.63). There was a 96.19% agreement between LARS-P and LARS-I. The identified cutoff point (>17) for LARS-P exhibited 77.14% sensitivity and 90% specificity in diagnosing apathetic and nonapathetic stroke survivors. CONCLUSIONS AND IMPLICATIONS: The LARS-P exhibits acceptable psychometric properties in stroke survivors, presenting a promising instrument for assessing apathy through a multidimensional framework.

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac.

BACKGROUND: Radiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals. METHODS: Dosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 - (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 - and AST 2 + groups and cutoff value calculations. RESULTS: Results showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71-0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models. CONCLUSIONS: This study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.

Aquaporin 4 beyond a water channel; participation in motor, sensory, cognitive and psychological performances, a comprehensive review.

Aquaporin 4 (AQP4) is a protein highly expressed in the central nervous system (CNS) and peripheral nervous system (PNS) as well as various other organs, whose different sites of action indicate its importance in various functions. AQP4 has a variety of essential roles beyond water homeostasis. In this article, we have for the first time summarized different roles of AQP4 in motor and sensory functions, besides cognitive and psychological performances, and most importantly, possible physiological mechanisms by which AQP4 can exert its effects. Furthermore, we demonstrated that AQP4 participates in pathology of different neurological disorders, various effects depending on the disease type. Since neurological diseases involve a spectrum of dysfunctions and due to the difficulty of obtaining a treatment that can simultaneously affect these deficits, it is therefore suggested that future studies consider the role of this protein in different functional impairments related to neurological disorders simultaneously or separately by targeting AQP4 expression and/or polarity modulation.

The effects of anxiety and external attentional focus on postural control in patients with Parkinson's disease.

BACKGROUND: Although anxiety is a common non-motor outcome of Parkinson's disease (PD) affecting 40% of patients, little attention has been paid so far to its effects on balance impairment and postural control. Improvement of postural control through focusing on the environment (i.e. external focus) has been reported, but the role of anxiety, as a confounding variable, remains unclear. OBJECTIVES: This study aimed to investigate the influence of anxiety and attentional focus instruction on the standing postural control of PD patients. METHODS: Thirty-four patients with PD (17 with high anxiety (HA-PD) and 17 with low anxiety (LA-PD)), as well as 17 gender- and age-matched healthy control subjects (HC) participated in the study. Postural control was evaluated using a combination of two levels of postural difficulty (standing on a rigid force plate surface with open eyes (RO) and standing on a foam surface with open eyes (FO)), as well as three attentional focus instructions (internal, external and no focus). RESULTS: Only the HA-PD group demonstrated significant postural control impairment as compared to the control, as indicated by significantly greater postural sway measures. Moreover, external focus significantly reduced postural sway in all participants especially during the FO condition. CONCLUSION: The results of the current study provide evidence that anxiety influences balance control and postural stability in patients with PD, particularly those with high levels of anxiety. The results also confirmed that external focus is a potential strategy that significantly improves the postural control of these patients. Further investigation of clinical applicability is warranted towards developing effective therapeutic and rehabilitative treatment plans.